Latent Semantic Analysis via Truncated ULV Decomposition

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dc.contributor.authorVarcin, Fatih
dc.contributor.authorErbay, Hasan
dc.contributor.authorHorasan, Fahrettin
dc.date.accessioned2020-06-25T18:17:00Z
dc.date.available2020-06-25T18:17:00Z
dc.date.issued2016
dc.departmentKırıkkale Üniversitesi
dc.description24th Signal Processing and Communication Application Conference (SIU) -- MAY 16-19, 2016 -- Zonguldak, TURKEY
dc.descriptionHorasan, Fahrettin/0000-0003-4554-9083; Erbay, Hasan/0000-0002-7555-541X
dc.description.abstractLatent semantic analysis (LSA) usually uses the singular value decomposition (SVD) of the term-document matrix for discovering the latent relationships within the document collection. With the SVD, by disregarding the smaller singular values of the term-document matrix a vector space cleaned from noises that distort the meaning is obtained. The latent semantic structure of the terms and documents is obtained by examining the relationship of representative vectors in the vector space. However, the computational time of re-computing or updating the SVD of the term-document is high when adding new terms and/or documents to pre-existing document collection. Thus, the need a method not only has low computational complexity but also creates the correct semantic structure when updating the latent semantic structure is arisen. This study shows that the truncated ULV decomposition is a good alternative to the SVD in LSA modelling about cost and producing the correct semantic structure.en_US
dc.description.sponsorshipIEEE, Bulent Ecevit Univ, Dept Elect & Elect Engn, Bulent Ecevit Univ, Dept Biomed Engn, Bulent Ecevit Univ, Dept Comp Engnen_US
dc.identifier.citationclosedAccessen_US
dc.identifier.endpage1336en_US
dc.identifier.isbn978-1-5090-1679-2
dc.identifier.scopus2-s2.0-84982788004
dc.identifier.scopusqualityN/A
dc.identifier.startpage1333en_US
dc.identifier.urihttps://hdl.handle.net/20.500.12587/6671
dc.identifier.wosWOS:000391250900310
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isotr
dc.publisherIeeeen_US
dc.relation.ispartof2016 24Th Signal Processing And Communication Application Conference (Siu)
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectLatent semantic analysisen_US
dc.subjectSingular value decompositionen_US
dc.subjectTruncated ULV decompositionen_US
dc.titleLatent Semantic Analysis via Truncated ULV Decompositionen_US
dc.typeConference Object

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